BACKGROUND: Limited research has examined what is actually done in the process of care by nursing staff in long-term institutional care. The applied instruments employed different terminologies, and psychometric properties were inadequately described. This study aimed to develop and test an observational instrument to identify and examine the amount of time spent on nursing interventions in long-term institutional care using a standardized language.METHODS: The Groningen Observational instrument for Long-Term Institutional Care (GO-LTIC) is based on the conceptual framework of the Nursing Interventions Classification. Developmental, validation, and reliability stages of the GO-LTIC included: 1) item generation to identify potential setting-specific interventions; 2) examining content validity with a Delphi panel resulting in relevant interventions by calculating the item content validity index; 3) testing feasibility with trained observers observing nursing assistants; and 4) calculating inter-rater reliability using (non) agreement and Cohen's kappa for the identification of interventions and an intraclass correlation coefficient for the amount of time spent on interventions. Bland-Altman plots were applied to visualize the agreement between observers. A one-sample student T-test verified if the difference between observers differed significantly from zero.RESULTS: The final version of the GO-LTIC comprised 116 nursing interventions categorized into six domains. Substantial to almost perfect kappa's were found for interventions in the domains basic (0.67-0.92) and complex (0.70-0.94) physiological care. For the domains of behavioral, family, and health system interventions, the kappa's ranged from fair to almost perfect (0.30-1.00). Intraclass correlation coefficients for the amount of time spent on interventions ranged from fair to excellent for the physiological domains (0.48-0.99) and poor to excellent for the other domains (0.00-1.00). Bland Altman plots indicated that the clinical magnitude of differences in minutes was small. No statistical significant differences between observers (p > 0.05) were found.CONCLUSIONS: The GO-LTIC shows good content validity and acceptable inter-rater reliability to examine the amount of time spent on nursing interventions by nursing staff. This may provide managers with valuable information to make decisions about resource allocation, task allocation of nursing staff, and the examination of the costs of nursing services.
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BACKGROUND: Recent evidence suggests that an increase in baccalaureate-educated registered nurses (BRNs) leads to better quality of care in hospitals. For geriatric long-term care facilities such as nursing homes, this relationship is less clear. Most studies assessing the relationship between nurse staffing and quality of care in long-term care facilities are US-based, and only a few have focused on the unique contribution of registered nurses. In this study, we focus on BRNs, as they are expected to serve as role models and change agents, while little is known about their unique contribution to quality of care in long-term care facilities. METHODS: We conducted a cross-sectional study among 282 wards and 6,145 residents from 95 Dutch long-term care facilities. The relationship between the presence of BRNs in wards and quality of care was assessed, controlling for background characteristics, i.e. ward size, and residents' age, gender, length of stay, comorbidities, and care dependency status. Multilevel logistic regression analyses, using a generalized estimating equation approach, were performed. RESULTS: 57% of the wards employed BRNs. In these wards, the BRNs delivered on average 4.8 min of care per resident per day. Among residents living in somatic wards that employed BRNs, the probability of experiencing a fall (odds ratio 1.44; 95% CI 1.06-1.96) and receiving antipsychotic drugs (odds ratio 2.15; 95% CI 1.66-2.78) was higher, whereas the probability of having an indwelling urinary catheter was lower (odds ratio 0.70; 95% CI 0.53-0.91). Among residents living in psychogeriatric wards that employed BRNs, the probability of experiencing a medication incident was lower (odds ratio 0.68; 95% CI 0.49-0.95). For residents from both ward types, the probability of suffering from nosocomial pressure ulcers did not significantly differ for residents in wards employing BRNs. CONCLUSIONS: In wards that employed BRNs, their mean amount of time spent per resident was low, while quality of care on most wards was acceptable. No consistent evidence was found for a relationship between the presence of BRNs in wards and quality of care outcomes, controlling for background characteristics. Future studies should consider the mediating and moderating role of staffing-related work processes and ward environment characteristics on quality of care.
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Background Clients facing decision-making for long-term care are in need of support and accessible information. Construction of preferences, including context and calculations, for clients in long-term care is challenging because of the variability in supply and demand. This study considers clients in four different sectors of long-term care: the nursing and care of the elderly, mental health care, care of people with disabilities, and social care. The aim is to understand the construction of preferences in real-life situations. Method Client choices were investigated by qualitative descriptive research. Data were collected from 16 in-depth interviews and 79 client records. Interviews were conducted with clients and relatives or informal caregivers from different care sectors. The original client records were explored, containing texts, letters, and comments of clients and caregivers. All data were analyzed using thematic analysis. Results Four cases showed how preferences were constructed during the decision-making process. Clients discussed a wide range of challenging aspects that have an impact on the construction of preferences, e.g. previous experiences, current treatment or family situation. This study describes two main characteristics of the construction of preferences: context and calculation. Conclusion Clients face diverse challenges during the decision-making process on long-term care and their construction of preferences is variable. A well-designed tool to support the elicitation of preferences seems beneficial.
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